Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory

• URL: http://arxiv.org/abs/2501.01710v1
• Date: Fri, 03 Jan 2025 09:10:56 GMT
• Title: Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
• Authors: Wei-Bin Kou, Qingfeng Lin, Ming Tang, Shuai Wang, Rongguang Ye, Guangxu Zhu, Yik-Chung Wu,
• Abstract summary: Large Vision Models (LVMs) as backbone and downstream perception head to understand AD semantic information. Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. experiments demonstrate that the proposed method improves the performance by over 66.48% and converges faster over 6 times.
• Score: 29.646749372031593
• License:
• Abstract: To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the the AD model's fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs' powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs' vast and diverse training data. On the other hand, to mitigate vehicles' computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48\% and converges faster over 6 times, compared to the existing state-of-the-art approach.

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